Association of Thallium with Diabetes Risk among Patients with Hearing Loss: Result from NHANES 2013 to 2018

To evaluate the correlation between thallium and diabetes risk among participants with hearing loss. This retrospective cohort study extracted related data such as demographic characteristics, lifestyle factors, and laboratory findings from the National Health and Nutrition Examination Survey (NHANES) database (2013–2018). Logistic regression analysis and interaction analysis were adopted to analyze the correlation between thallium and diabetes risk among patients with hearing loss. Then, the restricted cubic spline was employed to assess the nonlinear relationship between thallium and diabetes risk. The receiver operating characteristic curve and decision curve analysis were used to assess the predictive values of 3 multivariate models with or without thallium for diabetes risk. The Delong test was adopted to assess the significant change of the area under the curves (AUCs) upon thallium addition. A total of 425 participants with hearing loss were enrolled in the study: without diabetes group (n = 316) and diabetes group (n = 109). Patients with hearing loss in the diabetes group had significantly lower thallium (P < .05). The thallium was an independent predictor for diabetes risk after adjusting various covariates (P < .05). The restricted cubic spline (RCS) result showed that there was a linear correlation between thallium and diabetes risk (P nonlinear > .05). Finally, the receiver operating characteristic and decision curve analysis results revealed that adding thallium to the models slightly increased the performance in predicting diabetes risk but without significance in AUC change. Thallium was an independent predictor of diabetes risk among patients with hearing loss. The addition of thallium might help improve the predictive ability of models for risk reclassification. However, the conclusions should be verified in our cohort in the future due to the limitations inherent in the NHANES database.


Introduction
Hearing loss is the fourth leading contributor to years lived with disability, which represents a significant global health burden [1] About 1 billion young adults are at risk of hearing loss and there are 466 million individuals with disabling hearing loss worldwide reported by the World Health Organization. [2]Multiple factors such as aging, noise exposure, infection, and congenital may result in hearing loss. [1][5][6][7] Diabetes as a chronic multisystem disease is characterized by elevated glucose levels in the blood and urine because of inadequate production or use of insulin.Approximately 460 million adults have diabetes with a global incidence of 6.4%, which is expected to reach 7.7% by 2030. [8]Diabetes is related to neuropathy, retinopathy, kidney disease, and cardiovascular disease. [2]Many patients with diabetes have a probability of developing hearing loss since high glucose levels may damage the vessels of the nerves and stria vascularis. [9]However, lack of research focus on the diabetes risk among patients with hearing loss.Clinically, hearing loss refers to a pure tone average >25 dB; however, hearing conditions vary widely across grades, and mild hearing loss may be reversible after self-healing or modifying incentives. [10]Thus, we adopted data on hearing conditions with severe hearing difficulty as study populations.
Thallium is an extremely toxic heavy metal, independently discovered by Crookes and Lamy in 1861 from the dust of sulfuric acid burning equipment. [11]It is widely used in industrial activities such as electronic equipment, semiconductors, pesticides, and rodenticides. [12]Besides, anthropogenic activities also lead to increased concentrations of thallium in the environment, causing adverse health impacts on humans. [13,14]ince thallium is odorless, tasteless, and water-soluble, humans are under low-dose thallium exposure in their daily lives by eating contaminated food and water or the inhaling contaminated air. [15,16]Thallium induces the symptoms with 3 main categories: motor function impairment (muscle weakness and paralysis of the legs, etc.), gastrointestinal damage (vomiting and nausea, etc.), and nerve damage (anorexia nervosa and numbness, etc.). [17,18]Previous studies have demonstrated that exposure to heavy metals was a risk factor for metabolic abnormalities including hypertension, obesity, metabolic syndrome, and diabetes. [19,20][23][24] However, little is known about the association of thallium with diabetes risk among individuals with hearing loss.
Accordingly, this study was conducted to analyze the correlation between thallium and diabetes risk among participants with hearing loss by using the data from the National Health and Nutrition Examination Survey (NHANES) years 2013 to 2018.

Study population
The NHANES is a program of studies designed to assess the health and nutritional status of the population in the United States, which is implemented every 2 years (https://www.cdc.gov/nchs/nhanes/index.htm).The NHANES database can be acquired publicly from the National Center for Health Statistics of the Centers for Disease Control and Prevention (CDC).The survey protocol was approved by the National Center for Health Statistics Institutional Review Board, and written informed consent was obtained from all the participants.The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions.The examination component consists of medical, dental, and physiological measurements and laboratory tests.Our study extracted data from the 2013 to 2018 cycles.Totally 28,232 samples were obtained from 2013 to 2018.Exclusion criteria: without diabetes information (n = 529); without thallium information (n = 19,357); 7921 subjects having no hearing loss.Finally, 425 participants with hearing loss were enrolled in the study.

Assessment of hearing loss and diabetes
Hearing loss refers to serious difficulty hearing in this study.Trained interviewers provided survey participants with a household questionnaire about disability.Hearing loss is determined by affirmative responses to the question: Are you deaf or do you have serious difficulty hearing? [25]Diabetes is determined according to the answer to the following question: Other than during pregnancy, have you/has SP ever been told by a doctor or health professional that you have/he/she/SP has diabetes or sugar diabetes?

Covariates
We extracted the following data from the NHANES database.Demographic variables included age, gender, race, and poverty.The federal poverty level (FPL) referring to the ratio of family income to poverty, was used to assess the family's income conditions.Body measure index (BMI) was collected from the "Body measures" in the "Examination data."Laboratory data including thallium, albumin, alkaline phosphatase (AKP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), blood urea nitrogen, total calcium (TC), cholesterol, creatine phosphokinase (CPK), creatinine, triglycerides, and uric acid were collected from the "Laboratory data."The alcohol user was regarded as a participant who had drunk alcohol over the past 12 months.A smoker was regarded as a participant who had smoked at least 100 cigarettes in their entire life.Health insurance was evaluated by the following question: Are you/is SP covered by health insurance or some other kind of health care plan?Hypertension was determined by the answer to "Have you/has SP ever been told by a doctor or other health professional that you/s/he had hypertension, also called high blood pressure?"Detailed information can be accessed on the official website (https://www.cdc.gov/nchs/nhanes/index.htm).hypertension.Further, the restricted cubic spline was adopted to evaluate the nonlinear correlation between thallium and diabetes risk.Interaction analysis was performed to obtain the interactors for this correlation.Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curves were calculated to evaluate the predictive ability of each model, and the Delong test was employed to examine the change of AUCs upon thallium addition.Finally, ROC analysis and decision curve analysis were employed to compare the efficacy of models in predicting diabetes risk.
All data analyses were carried out by SPSS software version 23.0 and R software version 4.2.2.P < .05 was considered statistically significant.

Patient characteristics
Totally 425 patients with hearing loss were enrolled in the study and they were categorized into without diabetes group (n = 316) and with diabetes group (n = 109).Patient characteristics are shown in Table 1.Patients with diabetes are more likely to be older, nonalcoholic users, suffer from hypertension and have higher BMI (all P < .05).Besides, those with diabetes tend to exhibit significantly elevated levels of AST, ALT, and triglycerides but have lower cholesterol and thallium (all P < .05).However, no obvious differences in gender, race, income level, health insurance, smoker, albumin, AKP, blood urea nitrogen, TC, CPK, creatinine, and uric acid were observed between the 2 groups (all P > .05).

Association of thallium with diabetes risk among patients with hearing loss
We first treated thallium as a continuous variable to analyze its correlation with diabetes risk among patients with hearing loss.
In the univariate logistic regression analysis, as the thallium level increased, the risk of developing diabetes was decreased (P < .01).
After adjusting age, gender, race, income level, and BMI in multivariate model 1, thallium was remarkably related to a decreased diabetes risk with an odds ratio (OR) of 0.301.After adjusting covariates in model 1 as well as alcohol user, hypertension, health insurance, and smoking, the significance still existed in the correlation between thallium and diabetes risk (OR = 0.119) (P < .01).
After adjusting all covariates in multivariate model 3, thallium elevation was still an independent predictor for reduced diabetes risk (OR = 0.125) (P < .01).Then, we explored the association of thallium with diabetes risk when thallium was coded into 4 quartiles.Compared with patients in Q1, those in Q4 had a notably lower risk of developing diabetes in all models (all P < .05)except in multivariate model 2 (P > .05)(Table 2).Moreover, patients with elevated thallium levels tended to have lower odds of diabetes (P for trend < 0.05) (Table 2).
To further screen out the low-risk population of diabetes, subgroup analysis was performed.High thallium levels were closely linked to low diabetes risk among patients with hearing loss in spite of age, alcohol user, and smoker (all P < .05).Additionally, a significant negative relationship was found between thallium and diabetes risk among males, those with BMI ≥ 30, and with hypertension (all P < .05).However, this correlation was not significant in females, those with BMI < 25 group, 25 ≤ BMI < 30 group, and no hypertension group (all P > .05)(Table 3).Subsequently, we observed a linear relationship between the thallium and diabetes risk in the univariate model and adjusted model (P nonlinear > .05)(Fig. 1A, B).

Addictive value of thallium in diabetes risk prediction
The predictive value of each multivariate model was determined.The AUCs for the base model 1, model 2, and model 3 were 0.765, 0.798, and 0.828, respectively.After adding thallium to the models, the AUCs were changed to 0.782, 0.827, and 0.849, respectively, but without significant improvement (all P > .05)(Table 4) (Figs.2A-C).The decision curve analysis results further confirmed that the clinical efficacy of models had s slight increase after adding thallium (Fig. 2D-F).Although no significant improvement in model performance, thallium alone had a satisfactory performance in predicting diabetes risk among patients with hearing loss with an AUC of 0.718 (Figure S1, Supplemental Digital Content, http://links.lww.com/MD/L793).

Discussion
This retrospective study found that thallium was significantly associated with diabetes risk among participants with hearing loss, particularly in males, with a BMI ≥ 30, and with hypertension.This correlation remains significant regardless of age, alcohol user, and smoker.Besides, restricted cubic spline revealed a linear relation between thallium and diabetes risk in both the univariate model and the adjusted model.ROC analysis results showed that the addition of thallium to the multivariate models contributed to a slight increase of AUC in predicting diabetes risk but without significant change.
Industrial activities result in the release of approximately 5000 tons of thallium into the environment each year. [12]ncreased levels of thallium in farm animals, fruits, vegetables, and tap water may upregulated the risk of long-term low-dose exposure to thallium in the general population.[28] In this study, 0.15 and 0.11 μg/L were detected in urine samples of participants without and with diabetes group, respectively.As a heavy metal, thallium has a negative impact on human beings' health.Higher thallium concentrations during pregnancy were related to an increased risk of preterm birth, low birth weight, and premature rupture of membranes. [29,30]In addition, Qi et al [31] reported that pregnant women with thallium exposure might result in stunted growth in early childhood.Zhang et al [32] exhibited that pregnant women with the highest thallium level were at higher risk of developing gestational diabetes mellitus.Nevertheless, the correlation between thallium and diabetes risk among patients with hearing loss has not been clarified.
Thallium exposure could bring burdens to the stomach, leading to various gastrointestinal symptoms including anorexia and decreased appetite. [18]Li et al [33] reported that the food intake of mice was inhibited upon thallium exposure.Besides, the immune system including immune stimulation and immune inhibition can be affected upon exposure to heavy metals. [34]Immunoglobulin levels would be reduced in the blood after being exposed to lead. [35] cell apoptosis would increase and its development would be impaired after thallium exposure. [36]Thallium exposure induces reactive oxygen species excess in cells and promotes oxidative stress. [37]Then, Na+/K+-ATPase activity inhibition, and isolated mitochondrial swelling lead to damaged mitochondrial function, which is irreparable. [38][41] To maintain optimal physiological functions, mtDNAcn remains within a relatively stable range. [42]As a surrogate marker for mitochondrial function and intracellular oxidative stress, mtDNAcn is implicated in the pathogenesis of multiple diseases. [43]High mtDNAcn level was associated with a lower risk of depression and mortality. [44]Besides, individuals with elevated levels of mtDNAcn are at decreased risk of possessing cardiovascular disease. [45]Based on these findings, the authors speculated that thallium elevation results in mtDNAcn reduction, which might cause adverse impacts on health conditions.Surprisingly, we found that higher thallium levels were independently linked to lower diabetes risk among participants with hearing loss.However, the addition of thallium to 3 models led to an insignificant increase in predictive performance.Given that its lower levels were detected in both groups, thallium may not play a decisive role but a reference value in predicting diabetes risk.
For strengths, this is the first study to analyze the correlation between thallium and diabetes risk among patients with hearing loss.Besides, we adopted 3 different models with or without thallium to compare its value in predicting diabetes risk.Although the cross-sectional study designs can only infer correlation, but causality, this study provides a reference for clinical practice in the future.For limitations, this study may have potential biases in data collection or limitations inherent in the NHANES database.These findings especially the clinical relevance of adding thallium to the models should be validated in our cohort in the future.Further research could explore the biological mechanisms underlying the observed correlation between thallium and diabetes risk, providing a more comprehensive understanding of the relationship.
In conclusion, thallium is an independent predictor for diabetes risk; however, thallium may not play a decisive role but is a reference value in predicting diabetes risk among patients with hearing loss.

Figure 1 .
Figure 1.Restricted cubic spline (RCS) analysis for the nonlinear relationship between thallium and diabetes risk among patients with hearing loss.(A) Univariate model.(B) Adjusted model.

Figure 2 .
Figure 2. The predictive value of the models with or without thallium for diabetes risk among patients with hearing loss.Receiver operating characteristic curve analysis showing the predictive value of (A) model 1, (B) model 2, and (C) model 3. Model 1A: Model 1 without thallium; Model 1B: Model 1with thallium; Model 2A: Model 2 without thallium; Model 2B: Model 2 with thallium; Model 3A: Model 3 without thallium; Model 3B: Model 3 with thallium.
The authors have no funding and conflicts of interest to disclose.
The Ethics Committee of Longyan First Affiliated Hospital of Fujian Medical University deemed that this research is based on open-source data, so the need for ethics approval was waived.Supplemental Digital Content is available for this article.aOtolaryngology department, Longyan First Affiliated Hospital of Fujian Medical University, Longyan 364000, Fujian, China.

Table 1
Characteristics of the patients with hearing loss from NHANES 2013-2018.Categorical variables were represented by count (percent) and group comparisons were evaluated by the Chi-square test or Fisher test.The Shapiro-Wilk test was adopted to analyze the data distribution of continuous variables, which were presented as mean ± standard deviation for normally distributed data and median (interquartile range) for nonnormally distributed data.The comparisons for continuous variables were analyzed by Student t test (normal distribution) or Mann-Whitney U test (skewed distribution).The results showed that all the continuous variables were normally distributed except for thallium.Considering the skewed distribution of thallium, Log2-transformation was conducted to facilitate interpretation.Logistic regression analysis was employed to evaluate the correlation of thallium as a continuous variable and as quartiles with diabetes risk among participants with hearing loss.Univariate model; multivariate Model 1: demographic characteristics and BMI were adjusted; multivariate Model 2: the variables in Model 1 in addition to lifestyle factors, health insurance, and hypertension were adjusted; multivariate Model 3: adjusting the variables in Model 2 as well as laboratory findings.Then, the association of thallium with diabetes risk was conducted stratified by age, gender, BMI, alcohol use, smoking, and

Table 2
The association of Thallium with diabetes risk among patients with hearing loss.

Table 3
Subgroup analysis of the association of Thallium with diabetes risk among patients with hearing loss.

Table 4
Performance metrics of multivariate models with and without Thallium to predict diabetes among patients with hearing loss.
Model 1: adjusting demographic characteristics and BMI; Model 2: the variables in Model 1 in addition to lifestyle factors, health insurance, and hypertension were adjusted; Model 3: the variables in Model 2 as well as laboratory findings were adjusted AUC = are under the curve Li et al. • Medicine (2024) 103:9 Medicine